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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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RAP opportunity at National Institute of Standards and Technology NIST Characterization of Interfaces and Interphases in Polymeric Material Systems Location Engineering Laboratory, Materials and
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of aqueous chemical models, incorporated into computer codes, for both pure and applied research that include industrial chemistry, chemical engineering, water treatment, hydrometallurgy, toxicology, medical
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NIST only participates in the February and August reviews. Accurate measurement of optical radiation is important for the optical communications, medical device, semiconductor lithography
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terms in atomic displacements. The power of the CGFMD technique arises from the fact that the corresponding temporal equations can be solved exactly even for quadratic terms in displacements. This is in
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, molecular motions and collective membrane dynamics in these self assembled systems as a function of lipid composition as well as inclusions such as cholesterol and proteins and how those might be affected by
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the objective of commercialization for AST [5]. Research supporting this application and others is now transitioning back to NIST in collaboration with the Molecular Cellular and Developmental Biology Department
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RAP opportunity at National Institute of Standards and Technology NIST Enabling Science from Big Microscopy Image Data Location Information Technology Laboratory, Software and Systems Division
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of drug states present (free, bound, aggregated, etc.) is a primary objective. Thus, a tiered approach, whose basis is in the ability to characterize the (interfacial) properties of the nanoparticle systems
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processing equipment is available for prototyping of specialized nanostructures. key words Nanotechnology; Quantum nanowires; Wide band-gap semiconductors; Metamaterials; Power electronics; Eligibility